scholarly journals Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

2022 ◽  
Vol 27 (1) ◽  
pp. 1-30
Author(s):  
Mengke Ge ◽  
Xiaobing Ni ◽  
Xu Qi ◽  
Song Chen ◽  
Jinglei Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
Small World ◽  
Brain Network ◽  
Graph Processing ◽  
Hop Count ◽  
Scale Free ◽  
Large Scale Network ◽  
Average Latency ◽  
Scale Network ◽  
The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

scholarly journals Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation

2018 ◽  
Author(s):  
Ying-Qiu Zheng ◽  
Yu Zhang ◽  
Yvonne Yau ◽  
Yahar Zeighami ◽  
Kevin Larcher ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Structural Connectivity ◽  
Brain Network ◽  
Subcortical Structures ◽  
Neurodegenerative Process ◽  
Axonal Projections ◽  
Large Scale Network ◽  
Regional Vulnerability ◽  
Scale Network

AbstractIt is becoming increasingly clear that brain network organization shapes the course and expression of neurodegenerative diseases. Parkinson’s disease (PD) is marked by progressive spread of atrophy from the midbrain to subcortical structures and eventually, to the cerebral cortex. Recent discoveries suggest that the neurodegenerative process involves the misfolding and prion-like propagation of endogenous α-synuclein via axonal projections. However, the mechanisms that translate local “synucleinopathy” to large-scale network dysfunction and atrophy remain unknown. Here we use an agent-based epidemic spreading model to integrate structural connectivity, functional connectivity and gene expression, and to predict sequential volume loss due to neurodegeneration. The dynamic model replicates the spatial and temporal patterning of empirical atrophy in PD and implicates the substantia nigra as the disease epicenter. We reveal a significant role for both connectome topology and geometry in shaping the distribution of atrophy. The model also demonstrates that SNCA and GBA transcription influence α-synuclein concentration and local regional vulnerability. Functional co-activation further amplifies the course set by connectome architecture and gene expression. Altogether, these results support the theory that the progression of PD is a multifactorial process that depends on both cell-to-cell spreading of misfolded proteins and regional vulnerability.

scholarly journals Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

2021 ◽  
Author(s):  
Florian Krause ◽  
Nikolaos Kogias ◽  
Martin Krentz ◽  
Michael Luehrs ◽  
Rainer Goebel ◽  
...  
Keyword(s):  
Real Time ◽  
Executive Control ◽  
Large Scale ◽  
Acute Stress ◽  
Self Regulation ◽  
Real Life ◽  
Brain Network ◽  
Large Scale Network ◽  
Novel Approach ◽  
Scale Network

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG

2018 ◽  
Vol 32 (2) ◽  
pp. 304-314 ◽  
Author(s):  
Fali Li ◽  
Chanlin Yi ◽  
Limeng Song ◽  
Yuanling Jiang ◽  
Wenjing Peng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Motor Imagery ◽  
Large Scale ◽  
Brain Network ◽  
Network Reconfiguration ◽  
Scalp Eeg ◽  
Large Scale Network ◽  
Scale Network

scholarly journals How brain reacts to targeted attack at a hub region

2019 ◽  
Author(s):  
Wenyu Tu ◽  
Zilu Ma ◽  
Yuncong Ma ◽  
Nanyin Zhang
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Dorsal Anterior Cingulate Cortex ◽  
Node Activity ◽  
Large Scale Network ◽  
Scale Network ◽  
Network Properties ◽  
Multiple Species

AbstractThe architecture of brain networks has been extensively studied in multiple species. However, exactly how the brain network reconfigures when a local region stops functioning remains elusive. By combining chemogenetics and resting-state functional magnetic resonance imaging (rsfMRI) in awake rodents, we investigated the causal impact of acutely inactivating a hub region (i.e. dorsal anterior cingulate cortex) on brain network properties. We found that disrupting hub activity profoundly changed the function the default-mode network (DMN), and this change was associated with altered DMN-related behavior. Suppressing hub activity also impacted the topological architecture of the whole-brain network in network resilience, segregation and small worldness, but not network integration. This study has established a system that allows for mechanistically dissecting the relationship between local regions and brain network properties. Our data provide direct evidence supporting the hypothesis that acute dysfunction of a brain hub can cause large-scale network changes. This study opens an avenue of manipulating brain networks by controlling hub-node activity.

scholarly journals A New Algorithm for Controller Placement in SDN

2019 ◽  
Vol 8 (6) ◽  
pp. 1196-1201
Keyword(s):  
Large Scale ◽  
Optimum Number ◽  
Large Scale Network ◽  
Average Latency ◽  
Scale Network ◽  
And Performance ◽  
Multiple Controller ◽  
And Control ◽  
Multiple Scenarios ◽  
Installation Time

SDN network supports centralized network management by splitting control plane and data plane of forwarding devices and places the network intelligence in a software entity called controller. The controller can be placed in selective places of network to effectively monitor and control network activities. Large scale network needs multiple controller to manage control activities of network. In order to identify the optimum number of controllers and its effective locations in the network, a new algorithm is proposed using cut-vertex concept from graph theory. The proposed algorithm is simulated using Mininet SDN emulator. To study the performance of the proposed algorithm, multiple scenarios were used in the simulation and performance was analysed using parameters viz., flow installation time, average latency of network, throughput.

scholarly journals Large-Scale Network Reduction Towards Scale-Free Structure

2019 ◽  
Vol 6 (4) ◽  
pp. 711-723 ◽  
Author(s):  
Nicolas Martin ◽  
Paolo Frasca ◽  
Carlos Canudas-de-Wit
Keyword(s):  
Large Scale ◽  
Network Reduction ◽  
Scale Free ◽  
Large Scale Network ◽  
Scale Network

scholarly journals A macaque connectome for large-scale network simulations in TheVirtualBrain

2018 ◽  
Author(s):  
Kelly Shen ◽  
Gleb Bezgin ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Stefan Everling ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Function ◽  
Large Scale ◽  
Spatial And Temporal Scales ◽  
Multi Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Network Simulations ◽  
Dynamical Function ◽  
The Brain

AbstractModels of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

scholarly journals Efficient small-world and scale-free functional brain networks at rest using k-nearest neighbors thresholding

2019 ◽  
Author(s):  
Caroline Garcia Forlim ◽  
Siavash Haghiri ◽  
Sandra Düzel ◽  
Simone Kühn
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Small World ◽  
Nearest Neighbors ◽  
Resting State Fmri ◽  
Brain Network ◽  
K Nearest Neighbors ◽  
Scale Free ◽  
The Brain ◽  
Density Threshold

AbstractIn recent years, there has been a massive effort to analyze the topological properties of brain networks. Yet, one of the challenging questions in the field is how to construct brain networks based on the connectivity values derived from neuroimaging methods. From a theoretical point of view, it is plausible that the brain would have evolved to minimize energetic costs of information processing, and therefore, maximizes efficiency as well as to redirect its function in an adaptive fashion, that is, resilience. A brain network with such features, when characterized using graph analysis, would present small-world and scale-free properties.In this paper, we focused on how the brain network is constructed by introducing and testing an alternative method: k-nearest neighbor (kNN). In addition, we compared the kNN method with one of the most common methods in neuroscience: namely the density threshold. We performed our analyses on functional connectivity matrices derived from resting state fMRI of a big imaging cohort (N=434) of young and older healthy participants. The topology of networks was characterized by the graph measures degree, characteristic path length, clustering coefficient and small world. In addition, we verified whether kNN produces scale-free networks. We showed that networks built by kNN presented advantages over traditional thresholding methods, namely greater values for small-world (linked to efficiency of networks) than those derived by means of density thresholds and moreover, it presented also scale-free properties (linked to the resilience of networks), where density threshold did not. A brain network with such properties would have advantages in terms of efficiency, rapid adaptive reconfiguration and resilience, features of brain networks that are relevant for plasticity and cognition as well as neurological diseases as stroke and dementia.HighlightsA novel thresholding method for brain networks based on k-nearest neighbors (kNN)kNN applied on resting state fMRI from a big cohort of healthy subjects BASE-IIkNN built networks present greater small world properties than density thresholdkNN built networks present scale-free properties whereas density threshold did not

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